Data Science and Machine Learning using Python – A Bootcamp
Greetings,
I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?
This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.
Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is “Learn by Doing”!
For your satisfaction, I would like to mention few topics that we will be learning in this course:
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Basis Python programming for Data Science
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Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter
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NumPy
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Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions
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Pandas
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Pandas Data Structures – Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling – Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization
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Matplotlib
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Basic Plotting & Object Oriented Approach
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Seaborn
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Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics
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Plotly and Cufflinks
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Interactive & Geographical plotting
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SciKit-Learn (one of the world’s best machine learning Python library) including:
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Liner Regression
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Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models
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Logistic Regression
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Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision
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K Nearest Neighbour (KNN)
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Curse of Dimensionality, Model Performance
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Decision Trees
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Tree Depth, Splitting at Nodes, Entropy, Information Gain
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Random Forests
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Bootstrap, Bagging (Bootstrap Aggregation)
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K Mean Clustering
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Elbow Method
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Principle Component Analysis (PCA)
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Support Vector Machine
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Recommender Systems
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Natural Language Processing (NLP)
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Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature……..and MUCH MORE……….!
Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.
So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course…..!
Brief overview of Data around us:
According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation.
Have Fun and Good Luck!
Welcome, Course Introduction & overview, and Environment set-up
Data Science and Machine Learning using Python. NumPy, Pandas, Scikit-learn, Natural Language processing, NLTK, Recommender Systems, Decision Trees, Random Forests, Regression, Support Vector Machines
Please download the course material (link is provided below FAQ). The material includes all the resource and code files that you need for this course!
Frequently Asked Question (FAQ):
How to get successful in the filed of Data Science and Machine Learning, what is the probability of getting the job?
Few tips for you: Remember, you need to create your profile. I suggest, if you don’t have, create your GitHub account and upload all of your work there. As a data scientist, one of the key thing is reporting based on your findings. This course is an excellent jump-off to get into this rewarding career. After finished each project / section, create a report and present conclusions. Attach the report to each project in your Github account. You are most welcome to reference this course in your report, this might be helpful. Please don’t put the course material (the folder which came with this course) in your Github, create your own stuff (don’t worry even if they are similar), everything should go through your hands. I have seen that the blog posts are also helpful in creating your profile and to make good connections. Regarding job, there are more jobs in the market than the available professionals in this field. This course will land you in the interview, success is on your efforts and hard-work. Practice is a key and there are tons of datasets available to practice your skills. Keep yourself motivated, a great career is waiting for you!
Please follow the instructions to create an environment. Once, you are done creating environment, you don't need to install anything for this course. This is super easy!
Python Essentials
Python's key concepts for Data Science and Machine Learning, List comprehension, loops, lambda function. Hands-on with coding examples
Python for Data Analysis using NumPy
Numpy's arrays, useful methods and key concepts for Data Science and Machine learning
Python for Data Analysis using Pandas
This lecture will cover a fundamental and most useful concept of GroupBy in Pandas with visual explanation and code example.
Python for Data Visualization using matplotlib
This is a self-study and optional part.
Jupyter notebook is provided for you guys to learn few advanced plotting concepts using matplotlib.
Matplotlib provides tons of options that we don't use often, but it is good to know few of them.
Keep in in your mind, you can always explore the official documentation for more resources.Â
Python for Data Visualization using Seaborn
Python for Data Visualization using pandas
Python for interactive & geographical plotting using Plotly and Cufflinks
Capstone Project - Python for Data Analysis & Visualization
Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
Python for Machine Learning - scikit-learn - Logistic Regression Model
Python for Machine Learning - scikit-learn - K Nearest Neighbors
Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
You will create a machine learning model using Decision Tree and Random Forests using scikit-learn. One of the most important and key machine learning algorithm in business Data Science !
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